System for identifying individuals

ABSTRACT

A system for identifying individuals, which easily reveals the cause of misjudgment when it occurred, wherein individual identification means  4  identifies an individual by comparing an input image with a recognition dictionary  2 , wherein identification result analysis means  6  analyzes an identification result of the individual identification means  4 , and detects that portion of a region, used for identification, of the input image, which differs from the recognition dictionary  2 , wherein result output control means  5 , if an analysis result of the identification result analysis means  6  is that the input image differs from the dictionary image for the most part, directs only the input image to be displayed, and if the identification result is that the input image partially differs from the dictionary image, directs at least the input image and also the discordant portion to be displayed.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention relates to a system for identifingindividuals by comparing an input image with a dictionary imagepreviously stored.

[0003] 2. Related Art

[0004] A technology is well known by which an individual is identifiedfrom physical information (about the face, iris and fingerprints, forexample) of human beings and other animals. To cite an example, thistechnology is disclosed in U.S. Pat. No. 5,291,560.

[0005] In the technology revealed in the above literature, the image ofthe iris of an eye of a human being is converted into codes of 0 and 1,and the codes are stored as a dictionary. In an actual identificationprocess, an input iris image is converted into codes of 0 and 1, and thecodes are compared with codes stored in the dictionary to establish theidentity of an individual.

[0006] By the conversion of an input image into codes of 0 and 1, theamount of processing can be reduced, which leads to savings of storagecapacity. In the identification of an individual by fingerprints, too,it is ordinary to detect the features from an input image, and storeonly data on the features as a dictionary. Likewise, in theidentification of an individual by a face image, the amount ofinformation is reduced by the use of mosaic images or the facialfeatures.

[0007] In the conventional technology mentioned above, however, theinput image is converted into codes of 0 and 1 or information about thefacial features. Therefore, if the system should make an error inunderstanding data, it is difficult for a user without specializedtechnical knowledge to find the cause of the problem, the system will bea burden for the user to deal with.

[0008] To take an identification system using the face as an example, ifa smiling face is registered when a dictionary is created and a seriousface is input in the actual operation of the system, the system maymistake this as a discrepancy in the comparison step. On the other hand,if days passed from the registration till the identification operationand the user forgot his or her facial expression on the day ofregistration, it is impossible to conduct identification by simplydisplaying the registered image and the input image for comparison, withthe result that it is impossible to decide whether the identificationitself is correct or wrong.

[0009] In a system which has images at the time of dictionary creationstored in advance, if a misidentification occurred, even when the storedimage at issue is displayed, it takes time until you can find thatregion from the image of the whole face which was conducive to themisidentification.

[0010] Accordingly, it has long been desired that a system should bedeveloped which enables the user to find the cause of the misjudgmenteven when misidentification occurred.

[0011] With a system for identifying individuals by the prior artmentioned above, when this system is applied to identification ofanimals, such as dogs, cats or horses, a problem arises as follows.

[0012] Animals are different from human beings and you cannot expectthem to behave in a manner favorable to the system. For example, whentaking a picture of the iris of the eye with a video camera, a person isat a standstill squarely facing the camera. However, in the case of ananimal, it does not look at the camera, and the face is moving most ofthe time. Therefore, in the identification of animals, the images takenare inferior in picture quality, which is often responsible for afailure of identification. For this reason, it was necessary to takeimages repeatedly.

[0013] In this respect, it has been expected that a system foridentifying individuals should be created which can identify anindividual even when the image obtained does not have a good picturequality.

[0014] To solve this problem, the present invention adopts the followingarrangement.

SUMMARY OF THE INVENTION

[0015] <Arrangement in Claim 1>

[0016] A system for identifying individuals in Claim 1 comprises

[0017] an image memory means for storing an input image of an individualas an object to be identified;

[0018] a dictionary for having data on the features of collation objectsstored in advance;

[0019] a dictionary image memory means for storing dictionary images asa basis on which to extract data on the features of the collationobjects;

[0020] an individual identification means for analyzing the input imageheld in the image memory means and comparing said data on the featuresstored in the dictionary to thereby identify the individual;

[0021] an identification result analysis means for analyzing theidentification result by the individuals identifying means and detectingthat area of the region used for identification in the input image whichdoes not agree with the dictionary data;

[0022] a result output control means for, according to identificationresult by the individuals identifying means, issuing an analyze commandto the identification result analysis means and, according to analysisresult by the identification result analysis means, issuing a command todisplay the discordant portion to the identification result analysismeans, and deciding whether or not to display the input image and thedictionary image;

[0023] an image output control means for, according to a result ofdecision by the result output control means, controlling display of thediscordant portion, the input image and the dictionary image; and

[0024] a display for displaying an image outputted by the image outputcontrol means.

[0025] <Description of Claim 1>

[0026] The input image and the dictionary image are images of the irisof the human eye, and the individuals identifying means conductsidentification by comparing iris codes. However, any arrangement otherthan was described above may be applied so long as it identifies an isindividual.

[0027] The identification result analysis means detects an area wherethe input image does not coincide with the dictionary image by comparingiris codes, for example. The result output control means makes adecision to have at least the where discrepancy occurred displayedaccording to an analysis result of the identification result analysismeans.

[0028] By this operation, even when the identification of an individualfailed to establish the identity of the person, the where discrepancyoccurred is displayed, so that it is possible for the user to easilyguess the cause of misjudgment.

[0029] <Arrangement of Claim 2>

[0030] If the input image agrees with the dictionary data to such anextent that the identification result of the individuals identifyingmeans is larger than a predetermined threshold value in Claim 1, theresult output control means makes a decision not to issue an analyzecommand to the identification result analysis means nor issue a displaycommand to the image output control means.

[0031] <Description of Claim 2>

[0032] A case where the input image agrees with the dictionary data tosuch an extent that the identification result of the individualsidentifying means is larger than the predetermined threshold value meansthat the person is identified as a correct person. In this case, thesystem does not display analysis of the identification result nordisplay the input image. Therefore, unnecessary processing is notexecuted and a reduction of processing load can be expected.

[0033] <Arrangement of Claim 3>

[0034] In a system for identifying individuals according to Claim 1, ifthe input image agrees with the dictionary data to such an extent thatthe identification result of the individuals identifying means is higherthan the predetermined first threshold value but the amount of agreementis smaller than a second threshold value, which is smaller than thefirst threshold value, the result output control means makes a decisionnot to issue an analyze command to the identification result analysismeans nor issue a display command to the image output control means.

[0035] <Description of Claim 3>

[0036] The invention of Claim 3 is that the analyze process and theimage display process are not executed when the person is identified asa correct person or the person is identified as somebody else or thequality of the input image is judged to be inferior, and the analyzeprocess and the image display process are executed only when theidentity of the person is uncertain. The cases where the degree ofagreement between the input image and the dictionary data is less thanthe second threshold value include a case of no agreement at all.

[0037] By this arrangement, unnecessary processing can be omitted andsecurity can be secured even if a different person in bad faith attemptsto use the system.

[0038] <Arrangement of Claim 4>

[0039] In a system for identifying individuals according to any ofClaims 1 to 3, the identification result analysis means analyzes andjudges the discordant portion between the input image and the dictionaryimage to be larger than the predetermined value, the result outputcontrol means judges that the input image, for the most part, differsfrom the dictionary image and makes a decision to cause the input imageto be displayed.

[0040] <Description of Claim 4>

[0041] The invention in Claim 4 is such that according to the result ofthe analyze process by the identification result analysis means, if theresult output control means judges that the input image, for the mostpart, differs from the dictionary data, only the input image isdisplayed. In other words, in this case, the dictionary image is notdisplayed. Therefore, if the image is out of focus or blurred, the usercan easily guess the cause of misjudgment by looking at the input image.Furthermore, even when an ill-intentioned person tries to use thesystem, security can be maintained.

[0042] <Arrangement of Claim 5>

[0043] A system for identifying individuals in Claim 5 comprises

[0044] an image memory means for storing an input image of an individualto be identified;

[0045] a dictionary for having data on the features of collation objectsstored in advance;

[0046] a dictionary image memory means for storing dictionary images asa basis on which to extract data of the features of the collationobjects;

[0047] an individuals identifying means for analyzing the input imageheld in the image memory means and comparing the data on the features ofthe dictionary to thereby identify the individual;

[0048] an identification result analyzing means for analyzing theidentification result by the individuals identifying means and detectingthat area of the region used for identification in the input image whichdoes not agree with the dictionary data; and

[0049] a result output control means for making a decision not todisplay any image on the basis of a judgement that the input image, forthe most part, differs from the dictionary data if the discordantportion is larger than a predetermined value in the analysis of theidentification result analysis means.

[0050] <Description of Claim 5>

[0051] The invention in Claim 5 is such that as the result of theanalyze process by the identification result analysis means, if adecision is made that the input image, for the most part, differs fromdictionary data, no image is displayed. In this case, for example, whenan ill-intentioned person tries to access the system, security ispreserved because no input image is displayed and it is least likely tobe known how identification of an individual is carried out. Thisinvention is suitable for cases where emphasis is placed on thepreservation of security.

[0052] <Arrangement of Claim 6>

[0053] In a system for identifying individuals according to any ofClaims 1 to 5, the result output control means which makes a decision tocause both the input image and the discordant portion to be displayed ifit is judged in the analysis by the identification result analysis meansthat the input image partially differs from the dictionary image whenthe discordant portion is smaller than a predetermined value.

[0054] <Description of Claim 6>

[0055] The invention in Claim 6 is such that both the discordant portionand the input image are displayed in cases of identification by the irisin which the input image is a correct person's but there is a partialdifference between the input image and dictionary data when the iris ishidden behind the eye lid or eyelashes or light is reflected by the eyeto the camera. Thus, the above images serve as an effective clue bywhich to guess the cause of misjudgment by the user. If an operatorattends the system, he can make a final decision on the basis of theabove images.

[0056] <Arrangement of Claim 7>

[0057] The system for identifying individuals according to any of Claims1 to 5, wherein the result output control means which makes a decisionto cause only the discordant portion to be displayed on the ground thatthe input image partially differs from dictionary data when thediscordant portion is smaller than a predetermined value in the analysisby the identification result analysis means.

[0058] <Description of Claim 7>

[0059] The invention of Claim 7 displays only the discordant portion incontrast to Claim 6 that displays both the input image and thediscordant portion. This invention offers this effective clue by whichto guess the cause for the user to be unable to make a correctapprehension. If an operator attends the system, the above images serveas a basis on which he makes a final decision.

[0060] <Arrangement of Claim 8>

[0061] In a system according to any of Claims 1 to 7, the system foridentifying an individual is characterized in that the object to beidentified is the iris in the eye.

[0062] <Description of Claim 8>

[0063] The invention in Claim 8 is intended to identify individuals bycollation of iris images. The effect of this invention is that it ispossible to guess the cause of misidentification that even the user ishard to know, such as the iris being hidden behind the eyelid oreyelashes or the reflection of light by the eye to the camera whichhampers identification even when an input image of a correct person isused.

[0064] <Arrangement of Claim 9>

[0065] A system for identifying individuals in Claim 9 comprises

[0066] recognition dictionary for having stored in advance data onfeatures of an object to be identified and additional informationpeculiar to the object obtained from the object to be identified;

[0067] an individuals identifying means for identifying an individual bycomparing the data on features of the object to be identified with dataon features of the dictionary data;

[0068] an identification result decision means for, when having made adecision not to input additional information in a decision regardingwhether or not to input additional information according toidentification result by the individuals identifying means, outputtingidentification result by the individuals identifying means as a finalresult, or, when having made a decision to input additional information,issuing a command to obtain additional information and also a command toconduct a re-identification process, and making a decision to terminatethe re-identification process according to a result of there-identification process, and outputting a final identification result;

[0069] an additional information inputting means for obtaining arrivingadditional information upon receiving additional information from theidentification result decision means; and

[0070] a re-identification means for, on receiving a command to conducta re-identification process from the identification result decisionmeans, selecting the identification dictionaries containing all ofadditional information acquired by additional information inputtingmeans, and outputting as the result of re-identification a dictionaryhaving a closest possible value to data on the features of the objectunder identification among selected dictionaries.

[0071] <Description of Claim 9>

[0072] Individuals as objects to be identified are human beings, andanimals, such as dogs, cats and horses. And, individuals of any otherkinds may be identified. Among the methods for identifying individuals,there is the iris identify process using the iris of the eye. However,any other methods may be used. Furthermore, additional information isabout the external features of the object under identification, such asthe distinction of sex, the color of the eye, etc. Any other kind ofinformation, such as audio information may be used so long asindividuals have a peculiar and common feature.

[0073] The individuals identifying means conducts an identify process onan individual, the results of which are outputted to the identificationresult decision means. When the person is identified as a correct personaccording to the identification result of the individuals identifyingmeans, the identification result decision means outputs this informationas the final identification result. On the other hand, if the person isnot identified as a correct person, the a re-identification command isissued which is added with additional information. Additionalinformation input means obtains additional information entered by theuser or operator, and on the basis of this information, there-identification means executes a re-identification process. By usingthe re-identification result by the re-identification means, theidentification result decision means, if accordingly the person could beidentified, for example, outputs a decision to terminate there-identification process along with a final identification result.

[0074] Therefore, even if the identification of the person failed in theindividual identification process, re-identification is performedfocusing on certain dictionaries by using additional information, sothat identification of an individual is still possible even when anobtainable image has a poor picture quality.

[0075] <Arrangement of Claim 10>

[0076] In a system for identifying individuals according to Claim 9, adictionary has a number of different items of additional informationstored in advance, and the re-identification means conducts are-identification process each time it receives one item of additionalinformation obtained by additional information input means.

[0077] <Description of Claim 10>

[0078] The invention in Claim 10 is such that additional information isa plurality of items of information, such as the distinction of sex, thefur color, and the re-identification means conducts a re-identificationprocess each time it receives one item of additional information.Therefore, the reidentification process can be carried out even if allitems of information are supplied, and superfluous input operations neednot be performed.

[0079] <Arrangement of Claim 11>

[0080] In a system for identifyng individuals according to Claim 10, theidentification result decision means makes a decision to terminate thereidentification process each time the re-identification means conductsa re-identification process.

[0081] <Description of Claim 11>

[0082] The invention in Claim 11 is such that a decision to terminatethe re-identification process each time one item of additionalinformation is input. Therefore, when the person has been identified byinput of any additional information, it is not necessary to any morere-identification process, so that time spent for identification can beshortened.

[0083] <Arrangement of Claim 12>

[0084] In a system for identifying individuals according to Claim 11,the identification result decision means changes a criterion forre-identification at each execution of the re-identification process bythe re-identification means.

[0085] <Description of Claim 12>

[0086] The invention in Claim 12 has been made to enable the thresholdvalue to be varied to ease the criterion, for example, at eachre-identification process. Therefore, it becomes possible to enhance theprobability of successful identification of an individual whilesuppressing a deterioration of identification accuracy.

[0087] <Arrangement of Claim 13>

[0088] In a system for identifying individuals according to any ofClaims 9 to 13, identification of an individual is by collation of irisimages and additional information is about the external features of theindividual.

[0089] <Description of Claim 13>

[0090] The invention in Claim 13 is such that the process foridentifying an individual is done by collation of iris images andadditional information is about the external appearance of an individualunder identification. Therefore, even if an obtained image of the eye isinferior in picture quality, identification may succeed. Becauseadditional information is external features, even if the operator issupposed to confirm the entered additional information, the operator canconfirm it easily with fewer errors.

[0091] <Arrangement of Claim 14>

[0092] A system for identifying individuals in Claim 14 comprises

[0093] an image input unit for taking pictures of an individual as anobject to be identified from different camera angles and inputtingimages of the object;

[0094] an image analyzing means for analyzing the image and extractingthe features of the external appearance of the object; and

[0095] an output unit for obtaining a name by which to identify anindividual under identification according to the features.

[0096] <Arrangement of Claim 15>

[0097] In a system for identifying individuals according to Claim 14,the image analyzing means extracts the features by selecting a name of afeature corresponding to the image for each of the elements of externalappearance out of the names of predetermined features allotted tovariations of respective elements of the external appearance.

[0098] <Arrangement of Claim 16>

[0099] In a system for identifying individuals according to Claim 15,there is further provided a data base of data representing the featuresof the external appearance of a plurality of individuals by the names offeatures, the data being associated with the names of the individualswhen the data is accumulated, wherein the output unit is so arranged asto search the database, and obtain the names of individuals having thenames of features corresponding to the names of features selected at theimage analyzing means as the names of individuals under identification.

[0100] <Arrangement of Claim 17>

[0101] In a system for identifying individuals according to Claim 15 or16, the individuals to be identified and the plurality of objects areanimals, and wherein, the elements of the external appearance, which areused in the image analyzing means, the database and the output unit, aretwo or more items of the fur color, the white patterns of the head, thelocation of whirls, and white marks of the legs.

[0102] <Arrangement of Claim 18>

[0103] In a system for identifying individuals according to Claim 17,the image analyzing means extracts not less than two items of thelocation of a scar of an animal under identification and the conditionof spots other than white marks, wherein the database is associated withthe names of the plurality of animals, the location of scars or thecondition of spots of each animal when data is accumulated, and whereinthe output unit searches the database using the location of the scar orthe condition of spots extracted from the image to thereby obtain thename of the animal to be identified.

[0104] <Arrangement of Claim 19>

[0105] A system for identifying individuals in Claim 19 comprises

[0106] a name input means for entering the name of an individual underidentification;

[0107] an image input unit for inputting images of an individual underidentification by taking images of the object;

[0108] an image analyzing means for analyzing the image and extractingthe features of the external appearance of an individual underidentification;

[0109] a data base for accumulating data representing the features ofthe external appearance of individuals associated with the names of aplurality of individuals, and searching data corresponding to the namesinput through the name input means to thereby output the data;

[0110] a true/false decision unit for making a decision of whether anindividual under identification is true or not by comparing the featuresfound at said data base with the features extracted by said imageanalyzing means.

[0111] <Arrangement of Claim 20>

[0112] In a system for identifying individuals according to Claim 19,image analyzing means extracts the features by selecting a name of afeature corresponding to the image for each of the elements of externalappearance out of the names of predetermined features allotted tovariations of respective elements of the external appearance.

[0113] <Arrangement of Claim 21>

[0114] In a system for identifiying individuals according to Claim 20,the data base is accumulated in such a way that items of data on thefeatures of the external appearance of the plurality of individuals areassociated with the names of the individuals, the items of data beingrepresented by the names of the features, and the true/false decisionunit makes a decision of whether an individual under identification istrue or not by comparing names of the features found at the data basewith names of the features extracted by the image analyzing means.

[0115] <Arrangement of Claim 22>

[0116] In a system for identifying individuals according to Claim 20 or21, the individuals under identification and the plurality ofindividuals are animals, and the elements of the external appearance,which are used in the image analyzing means, the database and the outputunit, are two or more items of the fur color, the white patterns of thehead, the location of whirls, and white marks of the legs.

[0117] <Arrangement of Claim 23>

[0118] In a system for identifying individuals according to Claim 20 or21, the image analyzing means extracts not less than two items of thelocation of a scar and the condition of white marks of an animal underidentification, the database associates the names of the plurality ofanimals with the condition of scars of each animal or the condition ofspots when accumulating data, and the true/false decision unit makes adecision of whether the animal under identification is true or not fromthe location of scars or the condition of spots extracted from theimage.

[0119] <Arrangement of Claim 24>

[0120] In a system for identifying individuals according to any ofClaims 14 to 23, the image input unit is formed by a plurality ofcameras, disposed around the object under identification, for takingimages of the object under identification.

[0121] <Arrangement of Claim 25>

[0122] In a system for identifying individuals according to any ofClaims 14 to 23, the image input unit is formed by a camera movablearound an individual under identification to take images of the objectunder identification.

BRIEF DESCRIPTION OF THE DRAWINGS

[0123]FIG. 1 is a block diagram showing a first embodiment of theindividual identification system of the present invention;

[0124]FIG. 2 is a diagram for explaining an example of the recognitiondictionary in the first embodiment of the present invention;

[0125]FIG. 3 is a diagram for explaining an example of the dictionaryimage memory means in the first embodiment of the individualidentification system of the present invention;

[0126]FIG. 4 is a diagram for explaining an example of division of theiris region in the first embodiment of the individual identificationsystem of the present invention;

[0127]FIG. 5 is a diagram for explaining an example of calculationresults of Hamming distances of different regions in the firstembodiment of the individual identification system of the presentinvention;

[0128]FIG. 6 is a diagram for explaining an example of the result ofimage display in the first embodiment of the individual identificationsystem of the present invention;

[0129]FIG. 7 is a block diagram of a second embodiment of the individualidentification system of the present invention;

[0130]FIG. 8 is a diagram for explaining the contents of the recognitiondictionary in the second embodiment of the individual identificationsystem of the present invention;

[0131]FIG. 9 is a diagram for explaining the contents of theidentification result memory means in the second embodiment of theindividual identification of the present invention; and

[0132]FIG. 10 is a diagram for explaining the re-identification in thesecond embodiment of the present invention;

[0133]FIG. 11 is a block diagram of the individual identification systemaccording to a third embodiment of the present invention;

[0134]FIG. 12 is a diagram showing camera positions of the image inputportion in FIG. 11;

[0135]FIG. 13 is a diagram showing a horse photographed by cameras 32 to34 in FIG. 12;

[0136]FIG. 14 is a flowchart showing processes of color identificationunit 21 in FIG. 11;

[0137]FIG. 15 is a diagram showing the locations where the color isevaluated;

[0138]FIG. 16 is a diagram showing the names of the colors of horses;

[0139]FIG. 17 is a flowchart showing processes of the head white marksidentification unit 22 in FIG. 11;

[0140]FIG. 18 is a diagram showing the data extraction regions in FIG.17;

[0141]FIG. 19 is a diagram showing the white patterns (Part 1) of thehorse head;

[0142]FIG. 20 is a diagram showing the white patterns (Part 2) of thehorse head;

[0143]FIG. 21 is a diagram showing the names of the white patterns ofthe horse head;

[0144]FIG. 22 is a flowchart showing the processes of the whirlsidentification unit 23 in FIG. 11;

[0145]FIG. 23 is a diagram showing the locations where whirls aredetected;

[0146]FIG. 24 is a diagram showing the names and locations of whirls ofhorses;

[0147]FIG. 25 is a flowchart showing the processes of the leg whitemarks identification unit 24 in FIG. 11;

[0148]FIG. 26 is a diagram showing the data extraction regions in FIG.25;

[0149]FIG. 27 is a diagram showing names of the white marks of the legs;

[0150]FIG. 28 is a diagram showing the horse names and the feature namesaccumulated in the database 31 in FIG. 11; and

[0151]FIG. 29 is a block diagram of the individual identification systemshowing a fourth embodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0152] Embodiments of the present invention will be described in detailwith reference to the accompanying drawings.

[0153] <Embodiment 1>

[0154] <Composition>

[0155]FIG. 1 is a block diagram showing a first embodiment of the systemfor identifying individuals according to the present invention.

[0156] The system in FIG. 1 is a computer system and comprises imagememory means 1, a recognition dictionary 2, dictionary image memorymeans 3, individuals identifying means 4, result output control means 5,identification result analysis means 6, image output control means 7,and display device 8. In this embodiment, description will be made ofidentification of an individual by collation of iris images.

[0157] The image memory means 1 is installed in an auxiliary memorydevice, such as a semiconductor memory or a disk, and performs afunction to store input images of individuals as objects underidentification. The images are acquired through A/D conversion of imagestaken by the video camera, VTR, etc. The images held in this imagememory means 1 are used by the individuals identifying means 4 and theimage output control means 7.

[0158] The recognition dictionary 2 is installed in the auxiliary memorydevice, such as a semiconductor memory or a disk, and has data onfeatures of objects under identification stored in advance.

[0159]FIG. 2 is an explanatory diagram of the recognition dictionary 2.

[0160] As illustrated, the recognition dictionary 2 holds iris codes asdata on features and user information (name, sex, etc.).

[0161] The dictionary image memory means 3 is mounted in the auxiliarymemory device, such as a semiconductor memory or a disk, and holds theimages when respective dictionary were created and recorded in therecognition dictionary 2.

[0162]FIG. 3 is an explanatory diagram of the dictionary image memorymeans 3.

[0163] As illustrated, the dictionary image memory means 3 holds imagesentered at the times of creating dictionaries of the recognitiondictionary 2, and the respective images correspond to the respectivedictionaries stored in the recognition dictionary 2 on a one-to-onecorrespondence.

[0164] The individuals identifying means 4 analyzes an input image heldin the image memory means 1, and recognizes the iris by comparing itwith feature data of the recognition dictionary 2 to thereby identifythe individual. The results of the individuals identifying means 4 areutilized by the result output control means 5.

[0165] The result output control means 5 issues an analyze command tothe identification result analysis means 6 according to anidentification result of the individuals identifying means 4, and whenhaving sent the analyze command, in response to an analysis result fromthe identification result analysis means 6, makes a decision of whetheror not to display the input image held in the image memory means 1, thedictionary image stored in the dictionary image memory means 3 and ananalysis result. The result output control means 5 output a decisionresult to the image output control means 7.

[0166] On the basis of an identification result from the individualsidentifying means 4, the identification result analysis means 6 detectsthat area of the input image used for identification which does notagree with the dictionary image.

[0167] Also on the basis of a decision result from the result outputcontrol means 5, the image output control means 7 controls imagedisplay. The display device 8 is formed by a CRT or a liquid displaydevice, or the like, and displays the discordant portion.

[0168] <Operation>

[0169] First of all, suppose that the recognition dictionary 2 and thedictionary image memory means 3 each contain the users'iris codes andthe images entered at the times of dictionary creation storedpreviously. The method of generating iris codes in this case is awell-known one, and its description is omitted.

[0170] Also suppose that under the above condition, an image was inputand held in the image memory means 1. The individuals identifying means4 analyzes the input image held in the image memory means 1, andcompares it with dictionary data 2. This process will be describedbriefly. From the image of the eye held in the image memory means 1, acircumscribed circle of the pupil and a circumscribed circuit of theiris are obtained. The, polar coordinates are set relative to twocircles as the references, the iris region is divided into subdivisions,which are subjected to a filter process and a threshold process,outputted as codes consisting of 0 and 1 (hereafter referred to as iriscodes). Identification of an individual is performed by comparing thecodes, thus generated, with codes stored in the recognition dictionary2.

[0171] To cite an example, in the literature mentioned in the paragraphon the prior art, Hamming distances between the iris code created fromthe input image and the iris codes of the recognition dictionary 2 arecalculated, and a dictionary which brings about the smallest Hammingdistance is selected. (The Hamming distance at this time is calledHDMIN.) When the Hamming distance at this time is smaller than a presetthreshold value (HDTH), a decision is made that the person has beenidentified. The minimum value of Hamming distances during identificationHDMIN is used in the result output control means 5.

[0172] The identification result of the individuals identifying means 4is outputted to the result output control means 5 and the identificationresult analysis means 6. The operation of the result output controlmeans 5 will be described. The result output control means 5 makes adecision whether or not to analyze the identification result on thebasis of the identification result of the individuals identifying means4. For example, in this decision making, the result output control means5 uses the same threshold value that is used by the individualsidentifying means 4 for identification of a person. If the Hammingdistance HDMIN outputted by the individuals identifying means 4 islarger than the threshold value HDTH, the result output control means 5issues a command to the identification result analysis means 6 directingit to analyze the identification result. Analysis by the identificationresult analysis means 6 will be described later .

[0173] When the identification result analysis means 6 finishes theanalysis of the identification result, the result output control means 5decides a result output mode according to the analysis result. Forexample, if the identification result analysis means 6 makes a decisionthat the iris codes differ for the most part, the result output controlmeans 5 issues a command to the image memory means 1 directing it todisplay only the input image stored therein without displaying thedictionary image.

[0174] Because the input image is displayed, the user can getinformation about whether or not the failure of identification is due topoor picture quality (blur, out of focus, etc.) or due to his closingthe eyes, and so on. In this case, since the dictionary image is notdisplayed, security can be maintained even if a completely differentperson in bad faith should use the system.

[0175] Even when the identification result analysis means 6 decides thatthe iris codes differ partially, since there is a possibility that theuser is registered in the recognition dictionary 2, if the area wherethe iris codes differ is displayed, this will be helpful forconjecturing the cause of failure to recognize a correct user. If anoperator attends the system, when the system failed to recognize acorrect user, the operator can make a final decision according to adisplayed image. Therefore, the result output control means 5 directsthe image output control means 7 to output the ‘input image’, the‘dictionary image’, and the ‘area where the iris codes disagree’to thedisplay device 8.

[0176] The operation of the identification result analysis means 6 willbe described in detail.

[0177] The identification result analysis means 6 detects the area inthe input image used for identification which does not agree with thedictionary image on the basis of the identification result of theindividuals identifying means 4. Since identification of an individualby iris codes is performed in this embodiment, a decision is madewhether or not codes differ in the whole region or whether or not someareas agree even though there are discordant portions. Theidentification result analysis means 6 also detects the locations of thecoincident areas and the discordant portions.

[0178] If it is found by making those decisions that the iris codes donot agree on the whole, there is a possibility that the person underidentification is totally different from the person registered in thedictionary or the input image has a poor picture quality. On the otherhand, if the iris codes disagree in a limited area, a possibility isthat a part of the iris is hidden behind the eyelid or eyelashes. Inother words, though the person is a correct person, a wrong decision wasmade. In such a case, both the input image and the dictionary image canbe displayed to clarify the locations of coincident areas and discordantportions, by which the user can surmise the cause of erroneousrecognition. Again, if an operator attends the system, he can make afinal decision.

[0179] Description will now be made of the method of making a decisionof whether the iris codes differ for the most part or partiallyaccording to a result of iris recognition.

[0180]FIG. 4 is an explanatory diagram of a case where the iris regionis 32 subdivisions.

[0181] The number of subdivisions may be decided according to thepurpose of use, etc.

[0182] The identification result analysis means 6 calculates for therespective subdivisions Hamming distances generated from the Iris codesof a dictionary selected finally by the individuals identifying means 4and also from the input image.

[0183]FIG. 5 shows an example of a calculation result of Hammingdistances of the respective subdivisions.

[0184] Then, a number N1 representing those Hamming distances largerthan a predetermined threshold value TH1 is calculated. When N1 islarger than a predetermined threshold value TH2, a decision is made thatthe iris codes differ on the whole. In contrast, when N1 is smaller thanN2, a decision is made that the iris codes differ partially. When adecision of partial difference is made, the sub-region numbers at whichHamming distances are larger than TH1 are outputted to the image outputcontrol means 7.

[0185] The operation of the image output control means 7 will next bedescribed.

[0186] The image output control means 7 displays images in response to acommand from the result output control means 5. More specifically, theimage output control means 7 controls the display of input images heldin the image memory means 1 and images that are stored in the dictionaryimage memory means 3 and that correspond to a dictionary selectedfinally by the individuals identifying means 4. Furthermore, the imageoutput control means 7 displays a detection result of discordantsub-divisions outputted from the identification result analysis means 6.

[0187]FIG. 6 shows an example of an image display result.

[0188] The illustrated example is a case in which the sub-region numbers1, 3, 13, 15, 17, 19, 27, 29 and 31 are judged to be discordantsubdivisions when the iris region was divided as shown in FIG. 4 andanalyzed.

[0189] As is clear from this case, when comparing dictionary images withthe input image, the user or the operator needs to compare only thosesubdivisions where discordance occurred, so that the reason for failureof recognition can be surmised easily. In this example, the subdivisionswhere discordance occurred are obviously those that are hidden by theeyelid. Because the images agree in other subdivisions, even if thesystem could not judge the person to be a correct person, the operatorcan identify him as a correct person. In the case of FIG. 6, thediscordant sub-divisions are indicated by enclosing with a solid line,but the mode of display is not limited to this method. Any mode ofdisplay may be used so long as the discordant sub-divisions can benotified to the user.

[0190] Some of possible methods are as follows.

[0191] The discordant subdivisions lying successively are enclosed by asingle line as a single region.

[0192] An image showing only the discordant subdivisions is displayed.

[0193] The discordant subdivisions are highlighted (smoothening ofbrightness value, for example).

[0194] To show an illustrative example of individual identification, acase, which uses the iris, has been described. However, this embodimentmay be applied to individual identification processes for human beingsor animals, which uses analysis of images.

[0195] A modified embodiment of the present invention will be describedin the following.

[0196] In a certain type of application (an operator attends the systemat all times, for example), the input image and the dictionary image arealways displayed. (in this case, the result output control means can beobviated.)

[0197] When the analysis results of the identification result analysismeans 6 differ for the most part, neither the dictionary image nor theinput image is displayed. This is used in a case where emphasis isplaced on the preservation of security against a totally differentperson in bad faith tries to use the system.

[0198] A dictionary image is not displayed. Therefore, the dictionaryimage memory means 3 is obviated.)

[0199] The result output control means 5 has heretofore used onethreshold value in making a decision of whether or not to displayimages, but here uses a plurality of threshold values.

[0200] When a decision is made using two threshold values, for example,

HDMIN<HDTH1→No image displayed

HDTH1≦HDMIN<HDTH2→No image displayed

HDTH2≦HDMIN→No image displayed

[0201] For analysis by the identification result analysis means 6,images held in the image memory means 1 and images stored in thedictionary image memory means 3 are used. For example, in the firstembodiment, feature data, such as iris codes, is compared. However, incomparison between an input image and a dictionary image, the pixels ofmultiple gray levels may be compared.

[0202] <Effects>

[0203] As has been described, according to the first embodiment, evenwhen a correct person could not be identified in individualidentification, the input image, the dictionary image and the discordantportions are displayed to the user, and therefore the user can easilysurmise the cause of faulty recognition. When there is an operatorattending the system, even if the system failed to identify a correctperson, the operator can survey a displayed image, and thereby make afinal decision easily. Thus, it is possible to realize an individualidentification system for the user or the operator to operate veryeasily.

[0204] An input image and a dictionary image are displayed selectivelyafter the identification result is analyzed, so that security can bemaintained. In addition, images are not always displayed, and thereforehopes are held for effects of suppressing the amount of processing.

[0205] All operations of the first embodiment can be performed undercontrol of a computer program, which performs the function of theindividual identification system. Therefore, the individualidentification system according to the present invention can berealized, for example, by a method by recording the program on arecording medium, such as a floppy disc or a CD-ROM, and installing theprogram in a computer, or downloading the program from a network, or byanother method, or by another method of installing the program in a harddick or the like.

[0206] <Embodiment 2>

[0207] According to a second embodiment of the present invention,additional information is used which includes features peculiar toobjects under identification, such as the color of the eye, the lengthand the color of fur. Additional information is stored in a dictionaryused for individual identification, along with the quantities offeatures used for identification (iris codes, for example). Whenidentification failed, additional information is input sequentially, andthe identification process is performed again using only dictionarieshaving conditions confirming with the conditions of inputted additionalinformation. In the re-identification, the threshold values for adecision are varied in a sequential order.

[0208] <Arrangement>

[0209]FIG. 7 is a block diagram showing a second embodiment of theindividual identification system according to the present invention. Thesystem shown in FIG. 1 comprises a recognition dictionary 10,individuals identifying means 11, identification result memory means 12,identification result decision means 13, additional information inputmeans 14, and re-identification means 15. Regarding this secondembodiment, description will be made of a case in which the recognitionof the iris of an animal under identification is performed.

[0210] The recognition dictionary 10 is mounted in the auxiliary memorydevice, such as a semiconductor memory or a disc, and holds iris codesas feature sizes used for identification in the individuals identifyingmeans 11, and additional information about external features peculiar tothe object under identification.

[0211]FIG. 8 is a diagram for explaining the contents of the recognitiondictionary 10.

[0212] As illustrated, the recognition dictionary 10 holds iris codesand additional information about the distinction of sex, the color ofthe fur and the eye, and the tail.

[0213] The individuals identifying means 11 extracts feature data fromthe image of an individual under identification input from image inputmeans, not shown, and compares the feature data with iris codes from therecognition dictionary 10 to thereby identify the individual, andoutputs a result to the identification result memory means 12 and theidentification result decision means 13.

[0214] The identification result memory means 12 holds a result ofcomparison with dictionaries in the recognition dictionary 10 by theindividuals identifying means 11. In the case of this embodiment,Hamming distances are obtained as results, so that Hamming distances ofthe respective dictionaries are stored in the result memory means 12.

[0215] The identification result decision means 13 decides whether ornot to perform re-identification according to an identification resultof the individuals identifying means 11,and when having made decisionnot to input additional information, outputs the identification resultof the individuals identifying means 11 as the final result, and whenthe decision was not to input additional information, issues a commandto the additional information input means 14 directing it to acquireadditional information, and issues another command to there-identification means 15 directing it to perform a re-identificationprocess. Furthermore, the identification result decision means 13outputs a decision to terminate the re-identification process accordingto the inputted re-identification result and also outputs a finalidentification result.

[0216] The additional information input means 14 urges the user or theoperator to input additional information, and if additional informationis supplied, outputs the additional information to the re-identificationmeans 15. The additional information input means 14 is formed by adisplay device, such as a monitor, and input devices, such as a touchpanel, a keyboard and a mouse.

[0217] The re-identification means 15 starts to run in response to are-identification command from the identification result decision means13. On receiving each item of additional information from the additionalinformation input means 14, the re-identification means 15 selects thatdictionary (ID-No.) of the recognition dictionary 10 which covers alladditional information input heretofore, and reads the selecteddictionary and a Hamming distance as an identification result from theidentification result memory means 12, and selects a dictionary at theminimum Hamming distance, and outputs this value to the identificationresult decision means 13.

[0218] <Operation>

[0219] When image data of an individual under identification is input tothis system, the individuals identifying means 11 analyzes the imagedata to thereby obtain feature sizes, and compares the feature sizeswith the feature sizes registered in the recognition dictionary 10, andthus identifies the individual.

[0220] In the identification process, a circumscribed circle of thepupil and a circumscribed circle of the iris are obtained from the imageof the eye of the object under identification, the image being taken bythe a video camera. Then, polar coordinates are set with respect to twocircles as references, the iris region is divided into multiplesubdivisions, which receive a filter process and a threshold valueprocess, and are outputted as codes of 0 and 1 (hereafter referred to asiris codes). The iris codes thus generated and the iris codes stored inthe recognition dictionary 10 are compared to identify the individual.

[0221] For example, in the literature mentioned when the prior art wasreferred to, a Hamming distance is calculated between the iris codesgenerated from the input image and the iris codes of the recognitiondictionary 10, and a dictionary which brings about the minimum Hammingdistance is selected. (The Hamming distance at this time is designatedas HDMIN.) When the Hamming distance at this time is lower than apredetermined threshold value (HDTH), the individual is judged to be acorrect one. The minimum value HDMIN of Hamming distance duringidentification is used in the identification result decision means 13.The Hamming distance obtained by comparison with dictionaries of therecognition dictionary 10 is stored in the identification result memorymeans 12.

[0222]FIG. 9 is a diagram for explaining the contents of theidentification result memory means 12.

[0223] As shown in FIG. 9, a Hamming distance for each dictionary(ID-No.) of the recognition dictionary 10 is stored. Information storedin the identification result memory means 12 is used in there-identification means 15.

[0224] Description will then be made of a decision about identificationresults and the re-identification process.

[0225] The identification result decision means 13 decides whether ornot to input additional information on the basis of identificationresult of the individuals identifying means 11. When the minimum valueHDMIN of Hamming distance outputted by the individual identificationmeans 11 is smaller than a predetermined threshold value HDTH, thedecision means 13 decides not to input additional information. If adecision is made not to input additional information, the identificationresult outputted by the individual identification means 11 is outputtedas the final result.

[0226] In contrast, when the minimum value HDMIN of Hamming distance ishigher than the predetermined threshold value HDTH, the identificationresult decision means 13 uses the additional information input means 14to prompt the operator to supply additional information, and directs there-identification means 15 to perform the re-identification process.

[0227]FIG. 10 is a diagram for explaining the re-identification process.

[0228] Each time additional information is input to the additionalinformation input means 14, the re-identification means 15 selects adictionary (ID-No.) matching this additional information, and readsHamming distances between the selected dictionary and the identificationresult from the identification result memory means 12, obtains theminimum value HDMIN1 of Hamming distances, and outputs this value to theidentification result decision means 13.

[0229] The identification result decision means 13 decides whether ornot the obtained Hamming distance is smaller than the predeterminedthreshold value HDTH1(HDTH1>HDTH), and if it is smaller than thethreshold value, outputs a dictionary, which brings about the minimumvalue HDMIN1, as the final result, and decides that the individual hasbeen identified as the one registered in the dictionary 10. For example,in the example in FIG. 10, “Sex” was input as the first additionalinformation, and the operator selected “Female.” If the individual isjudged to be a registered individual, the identify process is finished.

[0230] On the other hand, also in the re-identification process in there-identification means 15, which includes the first additionalinformation, if the obtained HDMIN1 is larger than HDTH1, theidentification result decision means 13 directs the additionalinformation input means 14 to input the next additional information, andalso directs the re-identification means to perform there-identification process. When additional information is input, as withthe first information, the re-identification means 15 obtains anidentification result HDMIN2 covering the initially-input additionalinformation and additional information this time, and outputs HDMIN2 tothe identification result decision means 13. For example, in the exampleof FIG. 10, (A) shows a case where the individual was not identified asthe registered one. Suppose that the additional information input means14 prompts the operator to input “the color of the fur” as shown in (B),and the operator selects “White.”

[0231] The identification result decision means 13 decides whether ornot the HDMIN2 obtained by the re-identification process is smaller thanthe threshold value HDTH2(HDTH2>HDTH1), if HDMIN2 is found smaller,outputs a dictionary, which brings about the minimum value HDMIN2, asthe final result, and decides that the individual has been identified asthe one registered in the dictionary. On the other hand, if HDMIN2 islarger than HDTH2, the decision means 13 directs the additionalinformation input means 14 to input the next additional information, andalso directs the re-identification means 15 to perform are-identification process, so that the above-mentioned operation isrepeated.

[0232] As has been described, a more suitable dictionary is selectedeach time the operator inputs additional information, and a comparisonis made between the minimum value of Hamming distance obtained with theselected more suitable dictionary and a threshold value larger than theprevious set value, by which the individual is judged correct or false.Therefore, when the conditions are satisfied before the operator inputsall of additional information, the individual identification process isfinished. When the above-mentioned minimum value is not smaller than thepreset threshold value after all additional information has been input,the individual is judged to be not any of the individuals registered(not included in the recognition dictionary 10).

[0233] In the foregoing second embodiment, the individual underidentification is an animal, the operator inputs additional information.When the individual is a human being, however, it is possible for theperson to input additional information by himself. In this case,preferably, the operator manages the system to make sure that the inputadditional information is correct.

[0234] <Effects>

[0235] According to the second embodiment, even if the decision about anindividual's identity failed, the user or the operator is urged to inputadditional information, a dictionary is selected by using the inputadditional information, and an identity decision is made after thethreshold value for the decision is changed (relaxed little by little).Even when the system failed in the identity decision, it is stillpossible to make an identity decision on the individual.

[0236] Each time an item of additional information is input, a decisionis made by executing a re-identification process. Therefore, even beforeall additional information is input, it is possible to make a decisionon an individual's identity. Accordingly, redundant input operationsneed not be performed, with the result that time spent for a decisioncan be reduced.

[0237] Furthermore, because the identification result of the individualsidentifying means 11 is stored in the identification result memory means12, the individuals identifying means 11 need not execute the sameoperation during re-identification. Therefore, the amount of processingcan be reduced to a minimum, which contributes to the improvement of theprocessing speed. As a result, a high-performance system for individualidentification can be realized.

[0238] All operations in the second embodiment can be carried out bycontrol of a computer program to perform the function of the individualidentification system.

[0239] Accordingly, the individual identification system according tothe present invention can be materialized by a method by recording theprogram on a recording medium like a floppy disc and a CD-ROM,installing the program in a computer, or downloading the program from anetwork, or by another method of installing in a hard disc.

[0240] <Embodiment 3>

[0241]FIG. 11 is a block diagram of the individual identification systemaccording to a third embodiment of the present invention.

[0242] This individual identification system is a system for identifyingan individual racing horse, for example, and comprises an image inputunit 19, image analysis means 20 for analyzing an input image andextracting the features of the horse under identification, a horse nameoutput unit 30 connected to the image analysis means 20, and a database31 for providing the features of each individual to the horse nameanalysis unit 30.

[0243] The image input unit 19 accepts an image of a horse underidentification, and is formed of one or more cameras. The image analysismeans 20 is formed of a CPU and a memory, and includes a hair coloridentifier 21 which identifies a hair color of a horse from an imageinput from the image input unit 19. The hair color identifier 21 isconnected on its output side to a head white mark identifier 22. Thehead white mark identifier 22 is connected on its output side to a whirlidentifier 23. The whirl identifier 23 is connected on its output sideto a leg white mark identifier 24. A horse name output unit 30 isconnected to the output side of the leg white mark identifier 24 of theimage analysis means 20. The horse name output unit 30 outputs anidentification result of the horse under identification.

[0244] The operation of the individual identification system will bedescribed with reference to FIGS. 12 to 28.

[0245] FIGS. 12(a) and 12(b) show the locations of the cameras of theimage input unit 19 in FIG. 11. FIG. 12(a) is a side view and FIG. 12(b)is a top view. FIGS. 13(a) and 13(b) are views of the horse photographedby the cameras 32 and 34 in FIG. 12.

[0246] The cameras 32 to 34 of the image input unit 19 are arrangedaround the horse H as shown in FIGS. 12(a) and 12(b). Various focaldistances and apertures, which are the parameters of the cameras 32 to34 are provided to suit the distance to the horse H as the subject andthe magnification. The side cameras 32 located laterally of the horse Htake pictures of the body and side view of the legs of the horse H asshown in FIG. 13 (a). The front cameras 33 arranged in front of thehorse H take pictures of the face, the chest and the front view of thelegs. The rear cameras 33 located at the rear of the horse H takepictures of the buttock and the rear view of the legs of the horse H.FIGS. 12(a) and 12(b) show a case of using a plurality of cameras 32 to34, but the images corresponding to FIGS. 13(a) and 13(b) may be takenby moving one camera. The image input unit 19 outputs the images of thehorse under identification to the image analysis unit 20.

[0247]FIG. 14 is a flowchart showing the processes of the hair coloridentifier 21. FIG. 15 is a diagram showing the locations where haircolors are evaluated. FIG. 16 is a diagram showing the colors of thehorse.

[0248] The hair color identifier 21 refers to color data 21-1 and haircolor data 21-2 stored in memory, not shown, analyzes the images fromthe image input unit 19 by the processes S1 to S8 in FIG. 14, extractscolor names defined according to variations of the hair colors as theexternal appearance data of the horse, and outputs as a result of haircolor identification.

[0249] The specific color names of horses are kurige (chestnut),tochi-kurige (tochi-chestnut), kage (dark brown), kuro-kage (blackishdark brown), aokage (bluish dark brown), aoge (bluish dark brown), andashige (white mixed with black or brown) as shown in FIG. 16. The furcolor identification process S1 extracts color information of the bellyregion 41 of FIG. 15 from the supplied image, and compares it with colordata 21-1. In this comparison, a color closest to the region 41 isdetermined, and is extracted as an identification result of the furcolor. Horse fur color data, such as yellowing brown, reddish brown,black, white, brown, etc. is stored as color data 21-1. For the colorrepresentation method of color data 21-1, it is possible to use the RGBcolorimetric system using red (R), green (G) and blue (B) components ofthe pixels or the CIE-XYZ calorimetric system.

[0250] In the long hair color identification process S2 after the furcolor identification process S1, color information of the mane region 42and tail region 43 is extracted from the input image, and like in thefur color identification process S1, the color of long hair (mane andtail) of the horse H is extracted as an identification result. In thefour-leg lower portion fur color identification process S3, colorinformation of the leg regions 44, 45 in FIG. 16 is extracted from theinput image, and like in the fur color identification process S1, thefur color of the legs of the horse H is extracted as an identificationresult. In the eye peripheral region fur color identification processS4, color information of the fur of the surrounding region of the eye inFIG. 15 is extracted from the input image, and like in the fur coloridentification process S1, the fur color in the surrounding region ofthe eye of the horse H is extracted as an identification result. In theunderarm hair identification process S5, color information of theunderarm region 47 in FIG. 15 is extracted from the input image, andlike in the fur color identification process S1, the hair color of theunderarm of the horse H is extracted as an identification result. In thebelly fur color identification process S6, color information of thebelly region 48 in FIG. 15 is extracted from the input image, and likein the fur color identification process S1, the fur color of the bellyof the horse H is extracted as an identification result. In the noseperipheral region fur color identification process S7, color informationof the peripheral region 49 of the nose in FIG. 15 is extracted from theinput image, and like in the fur color identification process S1, thefur color of the peripheral region of the nose of the horse H isextracted as an identification result.

[0251] In the hair color decision process S8, the identified colors inthe processes S1 to S8 are compared with a combination of colors storedin hair color data 21-2 and the color of the horse H underidentification is decided. By this decision, the color of the fur of thehorse H is decided as “kurige”(chestnut),“tochi-kurige”(tochi-chectnut), “kage”(brown), “kurokage”(darker reddishbrown), “aokage”(dark-bluish black), “aoge”(bluish black) or“ashige”(white mixed with black or brown).

[0252]FIG. 17 is a flowchart showing the processes of the head whitemark identifier 22, and FIG. 18 is a diagram showing the extractionregions of FIG. 17. FIG. 19 is a diagram showing the white patterns(Part 1) of the horse's head, and FIG. 20 is a diagram showing the whitepatterns (Part 2) of the horse's head. FIG. 21 is a diagram showing thenames of the white marks of the head. The processes of the head whitemark identifier 22 will be described with reference to FIGS. 17 to 21.

[0253] The head white mark identifier 22 refer to data on head whitemarks 22-1 stored in memory, shown, extracts the white regions from theimage taken by the front camera 33 out of the images from the imageinput unit 19, analyzes the image in the processes S11 to S15 of FIG.17, obtains the names defined according to variations of the whitepatterns of the head as the external appearance data of the head, andoutputs as an identification result of the head white pattern. The namesof white patterns shown in FIG. 21 are stored in the head white markdata 22-1 along with the locations and sizes of the white marks.

[0254] The forehead white mark identification process S11 extracts thewhite region 51 in the forehead in FIG. 18 from the input image, andselects the name of the pattern by referring to the head white mark data22-1 and the shape, the size, and the presence or absence of a patternomission at the center portion. The patterns are divided into “star”,“large star”, “curved start”, “shooting star” or the like according tothe white pattern of the forehead of the horse H under identification.When the pattern has a round shape, its size is large, the number isone, and no omission of the central portion is found in measurement, the“large star” shown in FIG. 19 is selected as its pattern name, or if thepattern is small, the “star” or “small star” is selected. If the patternis not round and has a tailpiece, the “large shooting star” in FIG. 19is selected. Or, if the white pattern is curved, the pattern isclassified as a “curved star.” If the central portion is missing, thepattern is called a “ring star.”

[0255] In the nose bridge white mark identification process S12, thewhite region 52 on the bridge in FIG. 18 is extracted from the inputimage, the width of the white region is measured, and a pattern name inFIG. 20 is selected by referring to head white mark data 22-1. In thenose white mark identification process S13, the white region of the nose53 in FIG. 18 is extracted from the input image, the size of the whiteregion is measured, and a pattern name in FIG. 20 is selected byreferring to head white pattern data 22-1. In the lip white markidentification process S14, the white region of the lip 64 in FIG. 18 isselected from the input image, the width of the pattern is measured, anda pattern name is selected from FIG. 20 by referring to head white markdata 22-1. In the forehead-nose white mark identification process S15,the white region of the forehead-nose region in FIG. 18 is extractedfrom the input image, its size and width are measured, and a patternname is selected from FIG. 20 by referring to head white mark data 22-1.

[0256]FIG. 22 is a flowchart showing the processes of the whirlidentifier 23 in FIG. 11. FIGS. 23(a) to 23(d) are diagrams showing thelocations for detecting whirl patterns. FIG. 24 is a diagram showing thelocations and the names of whirls of the horse.

[0257] The whirl identifier 23 refers to data on whirl pattern 23-1 andwhirl location data 23-2 stored in memory, not shown, carries out theprocesses S21 and S22 in FIG. 22, and obtains whirl data of each horsefrom the images taken by the cameras 11 to 13 of the image input unit10.

[0258] Whirl pattern data for each fur color is stored in whirl patterndata 23-1, and the names and locations of whirls in FIG. 24 are storedin whirl location data 23-2. The numbers of the whirl locations in FIG.24 correspond to the numbers allocated to the portions of a horse inFIG. 23.

[0259] In the whirl extraction process S21 in FIG. 22, a whirl patternfor the fur color obtained by the fur color identifier 21 is selectedfrom the whirl pattern data 23-1, and locations 61 to 79 where there arewhirl patterns from the input image. In the whirl locationidentification process S22, whirl names are obtained from whirl locationdata 23-2 according to the whirl locations 61 to 79 obtained in thewhirl extraction process S21.

[0260]FIG. 25 is a flowchart showing the processes of the leg white markidentifier 24 in FIG. 11, FIG. 26 is a diagram showing the extractionregions in FIG. 25, and FIG. 27 is a diagram showing the names of whitemarks on the legs.

[0261] The leg white mark identifier 24 refers to the leg white markdata 24-1 stored in memory, not shown, extracts the white regions fromthe image from the image input unit 19, analyzes the image by theprocesses S31 and S32 in FIG. 25, obtains the names defined according tovariations of the leg white patterns as external appearance data of thehorse, and outputs as a result of let white mark identification. Thenames of white marks in FIG. 27 are stored in data on leg white marks24-1 along with the location and size of the white mark.

[0262] In the hoof white mark identification process S31 in FIG. 25, theleft white mark identifier 24 extracts the white mark in the region 81at the hoof in FIG. 26, measures its size and circumference length, andselects a right name for data on leg white mark 24-1. For example, ifthe area of the white mark is small, “tiny white” is selected. In theleg white mark identification process S32, the leg white mark identifier24 extracts the white mark in the leg region 82 in FIG. 26, measures thesize and the circumference length of the white region, and selects aright name for data on leg white mark 24-1.

[0263]FIG. 28 is a diagram showing a horse's name and its feature storedin database 31 in FIG. 11.

[0264] The horse name output unit 30 searches the database 31 by afeature name selected by the image analysis means 20. A plurality ofhorse names are associated with the features of the horses when they arestored in the database 31. Therefore, by searching the database 31 bythe names of features selected by the image analysis means 20, the nameof the horse under identification can be obtained. For example, when thenames of features selected and extracted by the portions 21 to 24 of theimage analysis means 20 are “kurige”(chestnut), “shooting starnose-bridge white”, “shumoku” and “right front small white”, the name ofthe horse is obtained as “abcdef.”

[0265] The individual identification system comprises aaan image inputunit 19 for taking images of a horse H under identification andacquiring images of the horse, image analysis means 20 for extractingthe external features peculiar to the horse, such as the fur color, headwhite mark, whirls, leg white mark from the image, and a horse nameoutput unit 30 for obtaining the name of the horse H by searching thedatabase 31. Therefore, it becomes possible to prevent the features frombeing overlooked, and preclude wrong decisions, such as misjudgment, sothat steady identification of horses can be performed without relying onthe skill of the operator.

[0266] Since the features are assigned feature names and compared, thefeatures of the horses can be easily stored in database 31 andmeasurements of horses can be obviated, which were required previously.Furthermore, a far smaller storage capacity is required of database 31than in the prior art in which a plurality of images of a horse werestored.

[0267] <Embodiment 4>

[0268]FIG. 29 is a block diagram of the individual identification systemaccording to a fourth embodiment of the present invention.

[0269] This individual identification system comprises the image inputunit 19, the image analysis means 20 and the database 31 like in thethird embodiment, and further includes a horse name input unit 90 and atrue/false decision unit 9l, which are not used in the precedingembodiments. The true/false decision unit 91 is provided in place of thehorse name output unit 30 in the third embodiment, and is connected tothe output side of the image analysis means 20, and also to the outputside of the database 31. The horse name input unit 90 is adapted tosupply the horse names to the database 31.

[0270] In the third embodiment, if the name of the horse H underidentification is not known, by extracting the features from the imagestaken, the name of the horse is found. In the individual identificationsystem according to the fourth embodiment, when the name of the horse tobe identified is already known, while pictures are being taken, adecision is made whether or not this horse coincides with the horse Hunder identification.

[0271] Regarding this individual identification system, description willbe made of the process in which a decision is made of whether or notthis horse corresponds to the horse H under identification.

[0272] First of all, the name of the horse to be identified is input tothe horse name input unit 90. The horse name input unit 90 sends thesupplied horse name to the database 31 directing it to search the namesof features corresponding to the name.

[0273] On the other hand, the image input unit 19 and the image analysismeans 20, by the same processes as in the third embodiment, select thenames of features as the external appearance data of the horse H toextract the features.

[0274] The true/false decision unit 91 compares the feature names of thehorse H fetched from the database 31 with the feature names suppliedfrom the image analysis means 20, and if they coincide or are mutuallyclose, outputs information that the horse H under identification in theprocess of picture taking corresponds to the horse whose name was inputto the horse name input unit 90. Or if they do not coincide, thetrue/false decision unit 91 outputs information that the horse does notcorrespond to the horse the name of which was input to the horse nameinput unit 90.

[0275] As has been described, the individual identification systemaccording to the fourth embodiment, which includes the image input unit19 and the image analysis means 20 and which is further added with thehorse name input unit 90 and the true/false decision unit 91, can decidewhether or not the horse under identification by comparing the featuresfrom the input image and the features obtained by inputting the horse'sname when the name is known. As a result, time required for searchingdata for identification of the horse can be reduced substantially.

[0276] The present invention is not limited to the above embodiments butvarious modifications are possible. Possible modifications are asfollows.

[0277] (1) In the image analysis means 20 according to the aboveembodiments, the fur colors, the head white patterns, whirl locations,or the leg white patterns are extracted, which represent the physicalfeatures of the horse H, and the names of the features are stored in thedatabase 31. However, some arrangement may be done such that the scarsand other marks may be extracted by image analysis and searched on thedatabase 31.

[0278] (2) In the third embodiment, database 31 is organized such that ahorse's name is obtained from the external physical features of thehorse, but data used for retrieval of a horse's name may include theregistration number, date of birth, blood type, pedigree registration,breed (e.g., thoroughbred male), producer, producer's address, producingdistrict, father's name, mother's name, etc.

[0279] (3) In the foregoing embodiments, description has been made ofthe individual identification system for a horse H, but this inventionmay obviously be applied to other animals so long as the animal hassimilar external features to those of the horses, and may further beapplied to things other than animals by using other external featuresand names.

[0280] As has been described, according to the third embodiment, theindividual identification system is formed by the output deviceincluding the image input unit for inputting images of an individualunder identification, which are taken from different angles; imageanalysis means for extracting the features of an individual underidentification obtained by image analysis of the input image; and anoutput unit added with a database. Therefore, identification of anindividual, a horse for example, can be performed steadily withoutoverlooking the features.

[0281] According to the fourth embodiment, the individual identificationsystem comprises the image input unit, the image analysis means, thename input means and the true/false decision unit, so that a decisioncan be made as to whether the individual under identification is true orfalse.

What is claimed is:
 1. A system for identifying individuals, comprising:an image memory means for storing an input image of an individual to beidentified; a dictionary for having data on the features of collationobjects stored in advance; a dictionary image memory means for storingdictionary images as a basis on which to extract data on the features ofsaid collation objects; an individuals identifying means for analyzingthe input image held in said image memory means and comparing said dataon the features stored in said dictionary to thereby identify theindividual; an identification result analyzing means for analyzing theidentification result by said individuals identifying means anddetecting that area of the region used for identification in said inputimage which does not agree with the dictionary data; a result outputcontrol means for, according to identification result by saidindividuals identifying means, issuing an analyze command to saididentification result analyzing mean and, according to analysis resultby said identification result analyzing means, issuing a command todisplay said area of disagreement to said identification resultanalyzing means, and deciding whether or not to display said input imageand said dictionary image; an image output control means for, accordingto a result of decision by said result output control means, controllingdisplay of said area of disagreement, said input image and saiddictionary image; and a display for displaying an image output by saidimage output control means.
 2. A system for identifying individualsaccording to claim 1 , wherein when said input image agrees with saiddictionary data to such an extent that the identification result of saidindividuals identifying means is larger than a preset threshold value,said result output control means makes a decision not to issue ananalyze command to said identification result analyzing means nor issuea display command to said image output control means.
 3. A system foridentifying individuals according to claim 1 , wherein said resultoutput control means makes a decision not to issue an analyze command tosaid identification result analyzing means nor issue a display commandto said image output control means when the identification result ofsaid individuals identifying means even if said input image agrees withsaid dictionary data to such an extent that the identification result ofsaid individuals identification means is larger than a first thresholdvalue preset, but if said first threshold value is smaller than a secondthreshold value.
 4. A system for identifying individuals according toany of claims 1 to 3 , wherein when said identification result analyzingmeans analyzes the area of disagreement between said input image andsaid dictionary data as larger than a preset value, said result outputcontrol means concludes that said input image generally differs fromsaid dictionary data and makes a decision to cause only said input imageto be displayed.
 5. A system for identifying individuals, comprising: animage memory means for storing an input image of an individual to beidentified; a dictionary for having data on the features of collationobjects stored in advance; a dictionary image memory means for storingdictionary images as a basis on which to extract data of the features ofsaid collation objects; an individuals identifying means for analyzingsaid input image held in said image memory means and comparing said dataon the features stored in said dictionary to thereby identify theindividual; an identification result analyzing means for analyzing theidentification result by said individuals identifying means anddetecting that area of the region used for identification in said inputimage which does not agree with said dictionary data; and a resultoutput control means for making a decision not to display any image onthe basis of a judgement that said input image generally differs fromsaid dictionary data if said area of disagreement is larger than apredetermined value in the analysis of said identification resultanalyzing means.
 6. A system for identifying individuals according toany of claims 1 to 5 , wherein there is provided result output controlmeans for making a decision to cause said input image and said area ofdisagreement to be displayed on the basis of a judgement that the inputimage partially differs from said dictionary data if the area ofdisagreement is smaller than a predetermined value in the analysis ofsaid identification result analyzing means.
 7. A system for identifyingindividuals according to any of claims 1 to 5 , wherein there isprovided result output control means for making a decision to cause onlysaid area of disagreement to be displayed on the basis of a judgementthat the input image partially differs from said dictionary data if thearea of disagreement is smaller than a predetermined value in theanalysis of said identification result analyzing means.
 8. A system foridentifying individuals according to any of claims 1 to 7 , wherein saidobject to be identified is an iris in the eye.
 9. A system foridentifying individuals, comprising: a recognition dictionary for havingstored in advance data on features of an object to be identified andadditional information peculiar to said object obtained from said objectto be identified; an individuals identifying means for identifying anindividual by comparing said data on features of said object to beidentified with data on features of said dictionary data; anidentification result deciding means for, when having made a decisionnot to input said additional information in a decision regarding whetherto input said additional information according to identification resultby said individuals identifying means, outputting identification resultby said individuals identifying means as a final result, or, when havingmade a decision to input said additional information, issuing a commandto obtain said additional information and also a command to conduct are-identify process, and making a decision to terminate said re-identifyprocess according to a result of the re-identify process, and outputtinga final identification result; an additional information inputting meansfor obtaining arriving additional information upon receiving saidadditional information from said identification result deciding means;and a re-identifying means for, on receiving a command to conduct are-identify process from said identification result deciding means,selecting said identification dictionaries containing all of saidadditional information acquired by said additional information inputtingmeans, and outputting as the result of re-identification a dictionaryhaving a closest possible value to data on the features of said objectunder identification among selected dictionaries.
 10. A system foridentifying individuals according to claim 9 , wherein said recognitiondictionary stores a plurality of items of additional information inadvance, and wherein said re-analyzing means conducts said re-identifyprocess at each reception of one item of said additional informationobtained by said additional information input means.
 11. A system foridentifying individuals according to claim 10 , wherein saididentification result deciding means makes a decision to terminate saidre-identify process at each re-identify process by said re-identifymeans.
 12. A system for identifying individuals according to claim 11 ,wherein said identification result deciding means changes a criterionfor re-identification at each re-identify process by said re-identifymeans.
 13. A system for identifying individuals according to any ofclaims 9 to 12 , wherein said identification of individuals is byidentification of the iris in the eye, and wherein said additionalinformation is about external features of an individual.
 14. A systemfor identifying individuals, comprising: an image input unit for takingpictures of an individual as an object to be identified from differentcamera angles and inputting images of said object; an image analyzingmeans for analyzing said image and extracting the features of theexternal appearance of said object; and an output unit for obtaining aname by which to identify an individual under identification accordingto said features.
 15. A system for identifying individuals according toclaim 14 , wherein said image analyzing means extracts said features byselecting a name of a feature corresponding to said image for each ofthe elements of external appearance out of the names of predeterminedfeatures allotted to variations of respective elements of said externalappearance.
 16. A system for identifying individuals according to claim15 , further comprising a data base of data representing the features ofthe external appearance of a plurality of individuals by said names offeatures, said data being associated with the names of said individualswhen said data is accumulated, wherein said output unit is so arrangedas to search said data base, and obtain the names of individuals havingthe names of features corresponding to the names of features selected atsaid image analyzing means as the names of individuals underidentification.
 17. A system for identifying individuals according toclaim 15 or 16 , wherein said individuals to be identified and saidplurality of objects are animals, and wherein, the elements of theexternal appearance, which are used in said image analyzing means, saiddata base and said output unit, are two or more items of the fur color,the white patterns of the head, the location of whirls, and white marksof the legs.
 18. A system for identifying individuals according to claim17 , wherein said image analyzing means extracts not less than two itemsof the location of a scar of an animal under identification and thecondition of spots other than white marks, wherein said data base isassociated with the names of said plurality of animals, the location ofscars or the condition of spots of each animal when data is accumulated,and wherein said output unit searches the data base using the locationof the scar or the condition of spots extracted from said image tothereby obtain the name of the animal to be identified.
 19. A system foridentifying individuals, comprising: a name input means for entering thename of an individual under identification; an image input unit forinputting images of an individual under identification by taking imagesof said object; an image analyzing means for analyzing said image andextracting the features of the external appearance of an individualunder identification; a data base for accumulating data representing thefeatures of the external appearance of individuals associated with thenames of a plurality of individuals, and searching data corresponding tothe names input through said name input means to thereby output saiddata; a true/false decision unit for making a decision of whether anindividual under identification is true or not by comparing the featuresfound at said data base with the features extracted by said imageanalyzing means.
 20. A system for identifying individuals according toclaim 19 , wherein said image analyzing means extracts said features byselecting a name of a feature corresponding to said image for each ofthe elements of external appearance out of the names of predeterminedfeatures allotted to variations of respective elements of said externalappearance.
 21. A system for identifying individuals according to claim20 , wherein said data base is accumulated in such a way that items ofdata on the features of the external appearance of said plurality ofindividuals are associated with said names of the individuals, saiditems of data being represented by said names of the features, andwherein said true/false decision unit makes a decision of whether anindividual under identification is true or not by comparing said namesof the features found at said data base with said names of the featuresextracted by said image analyzing means.
 22. A system for identifyingindividuals according to claim 20 or 21 , wherein said individuals underidentification and said plurality of individuals are animals, andwherein the elements of the external appearance, which are used in saidimage analyzing means, said data base and said output unit, are two ormore items of the fur color, the white patterns of the head, thelocation of whirls, and white marks of the legs.
 23. A system foridentifying individuals according to claim 20 or 21 , wherein said imageanalyzing means extracts not less than two items of the location of ascar and the condition of white marks of an animal under identification,wherein said data base associates the names of said plurality of animalswith the condition of scars of each animal or the condition of spotswhen accumulating data, and wherein said true/false decision unit makesa decision of whether the animal under identification is true or notfrom the location of scars or the condition of spots extracted from saidimage.
 24. A system for identifying individuals according to any ofclaims 14 to 23 , wherein said image input unit is formed by a pluralityof cameras, disposed around the object under identification, for takingimages of said object under identification.
 25. A system for identifyingindividuals according to any of claims 14 to 23 , wherein said imageinput unit is formed by a camera movable around an individual underidentification to take images of said object under identification.